implicit

class climlab.process.implicit.ImplicitProcess(**kwargs)

Bases: TimeDependentProcess

A parent class for modules that use implicit time discretization.

During initialization following attributes are intitialized:

Variables:

• time_type (str) – is set to 'implicit'

• adjustment (dict) – the model state adjustments due to this implicit subprocess

Attributes:

depth

Depth at grid centers (m)

depth_bounds

Depth at grid interfaces (m)

diagnostics

Dictionary access to all diagnostic variables

input

Dictionary access to all input variables

lat

Latitude of grid centers (degrees North)

lat_bounds

Latitude of grid interfaces (degrees North)

lev

Pressure levels at grid centers (hPa or mb)

lev_bounds

Pressure levels at grid interfaces (hPa or mb)

lon

Longitude of grid centers (degrees)

lon_bounds

Longitude of grid interfaces (degrees)

timestep

The amount of time over which step_forward() is integrating in unit seconds.

Methods

add_diagnostic(name[, value])	Create a new diagnostic variable called name for this process and initialize it with the given value.
add_input(name[, value])	Create a new input variable called name for this process and initialize it with the given value.
add_subprocess(name, proc[, verbose])	Adds a single subprocess to this process.
add_subprocesses(procdict)	Adds a dictionary of subproceses to this process.
compute()	Computes the tendencies for all state variables given current state and specified input.
compute_diagnostics([num_iter])	Compute all tendencies and diagnostics, but don't update model state.
declare_diagnostics(diaglist)	Add the variable names in inputlist to the list of diagnostics.
declare_input(inputlist)	Add the variable names in inputlist to the list of necessary inputs.
integrate_converge([crit, verbose])	Integrates the model until model states are converging.
integrate_days([days, verbose])	Integrates the model forward for a specified number of days.
integrate_years([years, verbose])	Integrates the model by a given number of years.
remove_diagnostic(name)	Removes a diagnostic from the process.diagnostic dictionary and also delete the associated process attribute.
remove_subprocess(name[, verbose])	Removes a single subprocess from this process.
set_state(name, value)	Sets the variable name to a new state value.
set_timestep([timestep, num_steps_per_year])	Calculates the timestep in unit seconds and calls the setter function of timestep()
step_forward()	Updates state variables with computed tendencies.
to_xarray([diagnostics, timeave])	Convert process variables to xarray.Dataset format.

_compute()

Computes the state variable tendencies in time for implicit processes.

To calculate the new state the _implicit_solver() method is called for daughter classes. This however returns the new state of the variables, not just the tendencies. Therefore, the adjustment is calculated which is the difference between the new and the old state and stored in the object’s attribute adjustment.

Calculating the new model states through solving the matrix problem already includes the multiplication with the timestep. The derived adjustment is divided by the timestep to calculate the implicit subprocess tendencies, which can be handeled by the compute() method of the parent TimeDependentProcess class.

Variables:

adjustment (dict) – holding all state variables’ adjustments of the implicit process which are the differences between the new states (which have been solved through matrix inversion) and the old states.

_update_diagnostics(newstate)

This method is called each timestep after the new state is computed with the implicit solver. Daughter classes can implement this method to compute any diagnostic quantities using the new state.